{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "os.chdir('D:/Practical Time Series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "djia_df = pd.read_excel('datasets/DJIA_Jan2016_Dec2016.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Change the row indices of the dataframe using the Date column\n",
    "djia_df.index = djia_df['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>17405.480469</td>\n",
       "      <td>17405.480469</td>\n",
       "      <td>16957.630859</td>\n",
       "      <td>17148.939453</td>\n",
       "      <td>17148.939453</td>\n",
       "      <td>148060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>17147.500000</td>\n",
       "      <td>17195.839844</td>\n",
       "      <td>17038.609375</td>\n",
       "      <td>17158.660156</td>\n",
       "      <td>17158.660156</td>\n",
       "      <td>105750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>17154.830078</td>\n",
       "      <td>17154.830078</td>\n",
       "      <td>16817.619141</td>\n",
       "      <td>16906.509766</td>\n",
       "      <td>16906.509766</td>\n",
       "      <td>120250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>16888.359375</td>\n",
       "      <td>16888.359375</td>\n",
       "      <td>16463.630859</td>\n",
       "      <td>16514.099609</td>\n",
       "      <td>16514.099609</td>\n",
       "      <td>176240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>16519.169922</td>\n",
       "      <td>16651.890625</td>\n",
       "      <td>16314.570313</td>\n",
       "      <td>16346.450195</td>\n",
       "      <td>16346.450195</td>\n",
       "      <td>141850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>2016-01-11</td>\n",
       "      <td>16358.709961</td>\n",
       "      <td>16461.849609</td>\n",
       "      <td>16232.030273</td>\n",
       "      <td>16398.570313</td>\n",
       "      <td>16398.570313</td>\n",
       "      <td>127790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>16419.109375</td>\n",
       "      <td>16591.349609</td>\n",
       "      <td>16322.070313</td>\n",
       "      <td>16516.220703</td>\n",
       "      <td>16516.220703</td>\n",
       "      <td>117480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>16526.630859</td>\n",
       "      <td>16593.509766</td>\n",
       "      <td>16123.200195</td>\n",
       "      <td>16151.410156</td>\n",
       "      <td>16151.410156</td>\n",
       "      <td>153530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>2016-01-14</td>\n",
       "      <td>16159.009766</td>\n",
       "      <td>16482.050781</td>\n",
       "      <td>16075.120117</td>\n",
       "      <td>16379.049805</td>\n",
       "      <td>16379.049805</td>\n",
       "      <td>158830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>2016-01-15</td>\n",
       "      <td>16354.330078</td>\n",
       "      <td>16354.330078</td>\n",
       "      <td>15842.110352</td>\n",
       "      <td>15988.080078</td>\n",
       "      <td>15988.080078</td>\n",
       "      <td>239210000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date          Open          High           Low         Close  \\\n",
       "Date                                                                            \n",
       "2016-01-04 2016-01-04  17405.480469  17405.480469  16957.630859  17148.939453   \n",
       "2016-01-05 2016-01-05  17147.500000  17195.839844  17038.609375  17158.660156   \n",
       "2016-01-06 2016-01-06  17154.830078  17154.830078  16817.619141  16906.509766   \n",
       "2016-01-07 2016-01-07  16888.359375  16888.359375  16463.630859  16514.099609   \n",
       "2016-01-08 2016-01-08  16519.169922  16651.890625  16314.570313  16346.450195   \n",
       "2016-01-11 2016-01-11  16358.709961  16461.849609  16232.030273  16398.570313   \n",
       "2016-01-12 2016-01-12  16419.109375  16591.349609  16322.070313  16516.220703   \n",
       "2016-01-13 2016-01-13  16526.630859  16593.509766  16123.200195  16151.410156   \n",
       "2016-01-14 2016-01-14  16159.009766  16482.050781  16075.120117  16379.049805   \n",
       "2016-01-15 2016-01-15  16354.330078  16354.330078  15842.110352  15988.080078   \n",
       "\n",
       "               Adj Close     Volume  \n",
       "Date                                 \n",
       "2016-01-04  17148.939453  148060000  \n",
       "2016-01-05  17158.660156  105750000  \n",
       "2016-01-06  16906.509766  120250000  \n",
       "2016-01-07  16514.099609  176240000  \n",
       "2016-01-08  16346.450195  141850000  \n",
       "2016-01-11  16398.570313  127790000  \n",
       "2016-01-12  16516.220703  117480000  \n",
       "2016-01-13  16151.410156  153530000  \n",
       "2016-01-14  16379.049805  158830000  \n",
       "2016-01-15  15988.080078  239210000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "djia_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HcRwnD+TSAlkP/E5tsKLewCVi40ZfjXWN3RXrvvhqgLBtADZ+75HYONNR//0PYGN/dw3T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\nLBBJUhMLRJLUxAKRJDX5Bd6N3ZR6x5WSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9a2aed438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "djia_df['Close'].plot(color='b')\n",
    "plt.title('Dow Jones Industrial Average between Jan 2016 - Dec 2016')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Closing Value')\n",
    "plt.savefig('plots/ch1/B07887_01_14.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lag = range(0,31)\n",
    "djia_acf = []\n",
    "for l in lag:\n",
    "    djia_acf.append(djia_df['Close'].autocorr(l))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pJO1hh8FGG/lGH0vqbYFHxOvZ059ExEuFD+AnrROemQF07Jhqpnzta7DnnjBv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9a203bf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.5, 5.5))\n",
    "plt.plot(djia_acf, marker='.', color='b')\n",
    "plt.title('Autocorrelation function for DJIA')\n",
    "plt.xlabel('Lag in terms of number of trading days')\n",
    "plt.ylabel('Autocorrelation function')\n",
    "plt.savefig('plots/ch1/B07887_01_15.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9a3f7e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Zo8JB10KaZjIfMRlEZNMDsJW2EWkVCgAiM3D3qc59Ikh+R+78y/dPttWdH0VKKQCIpNyd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\npRQARES6lAKAiEiX+v9bMfX48Pa/FQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9a3f7e080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot autocorrelation and confidence intervals using the plot_acf function\n",
    "plt.figure(figsize=(5.5, 5.5))\n",
    "plot_acf(djia_df['Close'], lags=20)\n",
    "plt.savefig('plots/ch1/B07887_01_16.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9a20437f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f9a20436a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot partial autocorrelation and confidence intervals using the plot_acf function\n",
    "plt.figure(figsize=(5.5, 5.5))\n",
    "plot_pacf(djia_df['Close'], lags=20)\n",
    "plt.savefig('plots/ch1/B07887_01_17.png', format='png', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
